AI-Driven Solid-State Battery Discovery & Health Prediction

• Understand solid-state battery technologies and solid electrolyte properties.
• Retrieve and curate battery-material data from Materials Project and OQMD.
• Generate materials descriptors using Pymatgen and Matminer.
• Develop machine-learning models for electrolyte-property prediction.
• Apply XGBoost, Random Forest and SHAP for prediction and explainability.
• Understand foundational Physics-Informed Neural Network (PINN) workflows.
• Analyze battery cycling data and degradation indicators.
• Develop SoH and RUL prediction workflows using time-series and LSTM modeling.

Day 1: Solid-State Battery Fundamentals & Materials Data Preparation

• Fundamentals of lithium-ion and solid-state batteries
• Solid electrolytes, ionic conductivity, stability, and key performance factors
• Introduction to AI and materials informatics in battery research
• Materials Project and OQMD for battery-material data
• Material structure processing using Pymatgen
• Feature and descriptor generation using Matminer
• Building an AI-ready solid-electrolyte dataset
• Basic visualization and screening of candidate materials

Hands-On: Create and explore an AI-ready solid-electrolyte materials dataset.

Day 2: Machine Learning & Explainable AI for Materials Discovery

• Data preprocessing and feature selection
• Ionic-conductivity and materials-property prediction
• Random Forest and XGBoost modeling
• Model validation using MAE, RMSE, R², and cross-validation
• Predicted vs actual performance analysis
• Explainable AI using SHAP
• Identification of important material descriptors
• AI-based ranking of promising solid-electrolyte candidates
• Introduction to physics-informed machine learning for battery research

Hands-On: Build, evaluate, and interpret an explainable AI model for solid-electrolyte screening.

Day 3: Battery Health, SOH & Remaining Useful Life Prediction

• Introduction to battery cycling and degradation datasets
• Capacity fade and battery-aging analysis
• Feature engineering from voltage, current, capacity, temperature, and cycle data
• State of Health (SOH) estimation and prediction
• Baseline ML models for battery-health prediction
• Time-series modeling using LSTM
• Battery degradation forecasting
• Remaining Useful Life (RUL) estimation
• Comparison of machine-learning and deep-learning models
• Research interpretation and identification of future experimental directions

• Extract and curate solid-state electrolyte data from research-grade materials databases.
• Generate materials descriptors and features using Pymatgen and Matminer.
• Develop and validate machine-learning models for electrolyte-property prediction.
• Interpret ML predictions using feature importance and SHAP-based explainable AI.
• Apply foundational PINN workflows to physics-informed battery modeling.
• Process and analyze battery cycling data to identify degradation and health indicators.
• Develop SoH and RUL prediction models using time-series and LSTM-based approaches.
• Build reproducible AI workflows in Google Colab for battery and materials research.
• Interpret model outputs in the context of battery materials, energy storage, and EV applications.

• MSc/M.Tech students in materials science, chemistry, physics, chemical engineering, energy engineering, or related fields
• PhD scholars and postdoctoral researchers in battery, materials, energy-storage, and computational research
• Faculty members, academicians, and research scientists
• Computational materials and machine-learning researchers
• Battery, EV, energy-storage, and BMS professionals
• Industry R&D professionals working in battery technologies and advanced energy systems

• Solid-state electrolyte dataset
• Materials feature/descriptors dataset
• ML-based property prediction model
• SHAP explainability analysis
• Physics-informed modeling workflow
• Battery degradation analysis
• SoH/RUL prediction model
• Reusable Google Colab notebooks